AIMC Topic: Datasets as Topic

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Uncovering the structure of self-regulation through data-driven ontology discovery.

Nature communications
Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issue...

Interpretable classification of Alzheimer's disease pathologies with a convolutional neural network pipeline.

Nature communications
Neuropathologists assess vast brain areas to identify diverse and subtly-differentiated morphologies. Standard semi-quantitative scoring approaches, however, are coarse-grained and lack precise neuroanatomic localization. We report a proof-of-concept...

An Open Science Approach to Artificial Intelligence in Healthcare.

Yearbook of medical informatics
OBJECTIVES: Artificial Intelligence (AI) offers significant potential for improving healthcare. This paper discusses how an "open science" approach to AI tool development, data sharing, education, and research can support the clinical adoption of AI ...

Towards interpretable machine learning models for diagnosis aid: A case study on attention deficit/hyperactivity disorder.

PloS one
Attention Deficit/Hyperactivity Disorder (ADHD) is a neurodevelopmental disorder that has heavy consequences on a child's wellbeing, especially in the academic, psychological and relational planes. The current evaluation of the disorder is supported ...

Viable and necrotic tumor assessment from whole slide images of osteosarcoma using machine-learning and deep-learning models.

PloS one
Pathological estimation of tumor necrosis after chemotherapy is essential for patients with osteosarcoma. This study reports the first fully automated tool to assess viable and necrotic tumor in osteosarcoma, employing advances in histopathology digi...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Frontiers in immunology
Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical element of successful vaccines and targeted immunotherapies. However, the underlying principles of this "convergence" of adaptive immunity among different...

Machine Learning for Diagnosis of Hematologic Diseases in Magnetic Resonance Imaging of Lumbar Spines.

Scientific reports
We aimed to assess feasibility of a support vector machine (SVM) texture classifier to discriminate pathologic infiltration patterns from the normal bone marrows in MRI. This retrospective study included 467 cases, which were split into a training (n...

Machine learning performance in a microbial molecular autopsy context: A cross-sectional postmortem human population study.

PloS one
BACKGROUND: The postmortem microbiome can provide valuable information to a death investigation and to the human health of the once living. Microbiome sequencing produces, in general, large multi-dimensional datasets that can be difficult to analyze ...

Applying deep matching networks to Chinese medical question answering: a study and a dataset.

BMC medical informatics and decision making
BACKGROUND: Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. It can be attributed to the di...